Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Network tomography is the study of a network's internal characteristics using information derived from end point data. The term tomography may be used to link the field conceptually to other processes that infer the internal characteristics of an object from external observation (from endpoints). Numerical treatments of network tomography generally break a network down into a graph and assemble a matrix of relationships between traffic statistics at known sources and destinations and the inferred relationships with traffic or delay on various network links. Such systems typically infer router link data and network traffic from other participants and generally decompose a system into linearly independent components to subtract undesired signals from desired ones and infer the statistics of target flows. Use of network tomography in the evaluation of datacenter networks may be impractical thus far because a user cannot control what links their data follows.
Datacenters are increasingly moving toward using software-defined networking (SDN), where datacenter networks can be rapidly reconfigured to reduce maximum transit time while retaining network capacity and reliability. SDN also allows datacenter users to explicitly control their own data forwarding and network paths. While datacenters implementing SDN may see benefits, they may also become vulnerable to certain side-channel attacks, such as those based on network tomography techniques.